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Computer Science > Computation and Language

arXiv:2408.15496 (cs)
[Submitted on 28 Aug 2024 (v1), last revised 1 Sep 2024 (this
version, v3)]

Title:ReMamba: Equip Mamba with Effective Long-Sequence Modeling

Authors:Danlong Yuan, Jiahao Liu, Bei Li, Huishuai Zhang, Jingang
Wang, Xunliang Cai, Dongyan Zhao
View a PDF of the paper titled ReMamba: Equip Mamba with Effective
Long-Sequence Modeling, by Danlong Yuan and 6 other authors
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    Abstract:While the Mamba architecture demonstrates superior
    inference efficiency and competitive performance on short-context
    natural language processing (NLP) tasks, empirical evidence
    suggests its capacity to comprehend long contexts is limited
    compared to transformer-based models. In this study, we
    investigate the long-context efficiency issues of the Mamba
    models and propose ReMamba, which enhances Mamba's ability to
    comprehend long contexts. ReMamba incorporates selective
    compression and adaptation techniques within a two-stage
    re-forward process, incurring minimal additional inference costs
    overhead. Experimental results on the LongBench and L-Eval
    benchmarks demonstrate ReMamba's efficacy, improving over the
    baselines by 3.2 and 1.6 points, respectively, and attaining
    performance almost on par with same-size transformer models.

Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2408.15496 [cs.CL]
          (or arXiv:2408.15496v3 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2408.15496
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Yuan Danlong [view email]
[v1] Wed, 28 Aug 2024 02:47:27 UTC (2,971 KB)
[v2] Thu, 29 Aug 2024 10:35:52 UTC (2,968 KB)
[v3] Sun, 1 Sep 2024 06:03:46 UTC (2,969 KB)
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